
# CONFIG -----------------------------------------------------------------------------------------------------------#

# Here are the input and output data paths (Note: you can override wav_path in preprocess.py)
data_path = 'dataset/'

# model ids are separate - that way you can use a new tts with an old wavernn and vice versa
# NB: expect undefined behaviour if models were trained on different DSP settings
voc_model_id = 'css_raw'


# DSP --------------------------------------------------------------------------------------------------------------#

# Settings for all models
sample_rate = 22050
n_fft = 2048
fft_bins = n_fft // 2 + 1
num_mels = 80
hop_length = 275                    # 12.5ms - in line with Tacotron 2 paper
win_length = 1100                   # 50ms - same reason as above
fmin = 40
min_level_db = -100
ref_level_db = 20
bits = 10                           # bit depth of signal
mu_law = True                       # Recommended to suppress noise if using raw bits in hp.voc_mode below
peak_norm = False                   # Normalise to the peak of each wav file


# WAVERNN / VOCODER ------------------------------------------------------------------------------------------------#


# Model Hparams
voc_mode = 'RAW'                    # either 'RAW' (softmax on raw bits) or 'MOL' (sample from mixture of logistics)
voc_upsample_factors = (5, 5, 11)   # NB - this needs to correctly factorise hop_length
voc_rnn_dims = 512
voc_fc_dims = 512
voc_compute_dims = 128
voc_res_out_dims = 128
voc_res_blocks = 10

# Training
voc_batch_size = 64
voc_lr = 1e-3
lr_decay = 0.5         
lr_decay_start = 100000 
lr_decay_each = 100000
weight_decay = 1e-6
voc_checkpoint_every = 25_000
voc_gen_at_checkpoint = 10          # number of samples to generate at each checkpoint
voc_total_steps = 1_000_000         # Total number of training steps
voc_test_samples = 50               # How many unseen samples to put aside for testing
voc_pad = 2                         # this will pad the input so that the resnet can 'see' wider than input length
voc_seq_len = hop_length * 5        # must be a multiple of hop_length
voc_clip_grad_norm = 4              # set to None if no gradient clipping needed

# Generating / Synthesizing
voc_gen_batched = True              # very fast (realtime+) single utterance batched generation
voc_target = 11_000                 # target number of samples to be generated in each batch entry
voc_overlap = 550                   # number of samples for crossfading between batches
